Intelligent Building Damage Detection via Integrated Spatial Attention and Dilated Convolution

To address the limitations of existing models in multi-scale feature extraction and boundary localization, this study proposes DeepCrack, a deep learning model that integrates a spatial attention mechanism with dilated convolutions to enhance automation and accuracy in building damage detection. The model adopts a lightweight MobileNetV3 as its backbone, incorporating FPN-based multi-scale feature fusion, a DeepLabV3+ atrous convolution module, boundary-aware loss optimization, and CRF-based post-processing to form an efficient segmentation framework. Performance evaluations and ablation studies conducted on the Crack500 dataset demonstrate that DeepCrack outperforms UNet, PSPNet, and DeepLabV3+ in terms of precision, recall, and IoU. Each integrated module contributes significantly to performance gains. The proposed model offers a scalable and high-performance solution for intelligent structural damage recognition.